جستجوی مقالات مرتبط با کلیدواژه « vegetation cover » در نشریات گروه « جغرافیا »

Water stress is a major environmental issue and has a significant impact on the sustainability of urban areas worldwide. This study examines the changes in land surface temperature (LST) and their relationship with variations in vegetation cover during wet and dry periods in Zayandeh Rood. To accomplish this, Landsat TM, ETM, and OLI satellite images from three dry years (2001, 2009, and 2018) and three wet years (2005, 2006, and 2020) were used. The research findings indicate that, in 2006, when water was flowing in the river bed, vegetation cover decreased from 36% (201 km2) to 23% (km2) in 2018, when the river bed had no water. Over the last two decades, the Earth's surface temperature has increased. The highest average temperature was observed in 2018, at 40/4 degrees Celsius, and the maximum temperature in the dry years of 2001, 2009, and 2018 was higher than in the wet years of 2005, 2006, and 2020. The highest density of heat islands was observed in regions 2, 4, 5, 6, 7, 14, and east of region 15, which is concentrated on barren lands and then on urban areas. In the dry year of 2009, the total area of temperature classes 42-49 increased by approximately 12% compared to the dry year of 2001 and also increased by about 25% in 2018. In contrast, it was zero in the wet years of 2005 and 2006, and in 2020, it decreased by around 34% compared to 2018. Furthermore, urban development, which has grown by 7/3% over the past two decades, has contributed to the reduction of vegetation and intensified the urban heat island effect. Examining the average temperature changes at different distances from the Zayande Rood indicated that the temperature increased by about 1 degree Celsius with the distance from the river.

Climate change refers to unusual shifts in the earth's atmospheric climate that have far-reaching consequences across the globe. These shifts have led to an increase in the average temperature of the earth's surface, which has created numerous challenges for human security worldwide. Climate change has a significant impact on the hydrological cycle, causing changes in water resources, and leading to an increase in the frequency and intensity of droughts and floods. Water stress, one of the major environmental problems, has significant effects on the sustainability of urban areas across the world. The city of Isfahan has experienced many challenges, such as a rise in temperature, as a result of climatic changes and the drying up of the Zaynde-Rood River. Therefore, examining temporal and spatial changes in land use and their effects on the earth's surface temperature provides a clear picture of these changes. This analysis presents the behavior of the data before and after the drying of the Zayandeh Rood.

For this research, we used 6 Landsat satellite images during the summer season, as it has the maximum vegetation cover. We obtained the images of TM, ETM, and OLI sensors of the Landsat satellite from the Google Earth Engine system (https://code.earthengine.google.com). We calculated and extracted the LST of each image to investigate temperature changes in different areas of the city. We calculated the changes in vegetation area during the dry years of 2001, 2009, and 2018, and the wet years of 2005, 2006, and 2020, and their effects on the surface temperature using the NDVI index. We also calculated the rate of urban growth and expansion using the NDBI index. All the calculations, graphs, and analyses were done using ArcGIS Pro and Excel.

The catchment area of Zayandeh Rood has experienced drought in recent years, causing the water flow of Zayandeh Rood to stop flowing continuously in the metropolis of Isfahan. This has led to significant changes in the natural vegetation and planting in terms of land area. The complete interruption of the river water flow has affected the amount of temperature, evaporation, and transpiration of plants in the Isfahan metropolis, which has subsequently affected the extent of vegetation cover. Research shows that in 2006, when water was flowing in the riverbed, the vegetation area decreased from 36% (201 km2) to 23% (126 km2) in 2018, when the riverbed was devoid of water. This was due to the expansion of residential areas and the change of agricultural uses to residential. Over the last two decades, the temperature of the earth's surface has increased, with the highest average temperature observed in 2018 at 40.4 degrees Celsius. The maximum temperature in the dry years of 2001, 2009, and 2018 was higher than the wet years of 2005, 2006, and 2020. The highest density of heat islands was observed in regions 2, 4, 5, 6, 7, 14, and east of region 15, which is concentrated on barren lands. The total area of the temperature classes 42-49 in the dry year of 2009 increased by about 12% compared to the dry year of 2001 and also in 2018 by about 25%, while in the wet years of 2005 and 2006, it was zero. In 2020, compared to 2018, it decreased by about 34%, and in 2020 compared to 2009 and 2018, it increased by about 11% on average, indicating an increase in temperature in dry years and a decrease in temperature in the wet years. Urban development has increased by 3.7% over the past two decades, which has contributed to reducing vegetation and intensifying the urban heat island effect. Examining the average temperature changes at different distances from the Zayande Rood showed that the temperature increased by about 1 degree Celsius with the distance from the river.

The analysis of NDVI data revealed that vegetation significantly decreased during dry years compared to wet years. The area of weak vegetation increased by 3%, indicating that water stress led to a shift towards vegetation types that require less water. The examination of the earth's surface temperature also showed a clear difference between dry and wet years. During dry years, LST values were generally higher than in wet years, with an average temperature of 40.4 degrees Celsius in 2018. In contrast, wet years showed lower LST values, with an average temperature of 31.4°C in 2006. The distribution of LST was also different between dry and wet years, with dry years characterized by a wider range of LST values, indicating the urban heat island effect. Water stress emerged as a crucial factor affecting vegetation and LST. Additionally, urban development, which has increased by 3.7 percent over the past two decades, has contributed to reducing vegetation and intensifying the urban heat island effect.

Drought is one of the most complex natural disasters that causes economic, social and environmental damage and is often described as a creeping phenomenon. Drought monitoring using satellite images can represent the severity of drought in areas with a lack of meteorological precipitation data and compensate for its spatial and temporal deficiency. In this research, the drought of Golestan province has been investigated using SPI, TCI, VCI and VHI indices and with the help of MODIS sensor satellite images. Therefore, first, VHI, VCI and TCI drought index maps were extracted. The findings in the TCI index study showed that in 2000, more than 80% of the studied area experienced severe drought. Similarly, in 2010, 2017 and 2018, a significant part of the studied area was in a severe drought situation. By examining the VCI index, 2008, and 2011. The maps also show that a very severe meteorological drought occurred in 2008. In the study of the VHI index during a period of 21 years in the studied area, it showed that the years 2000, 2001, 2002, 2008, 2010, 2011, 2014, 2015, 2017, 2018 and 2021 have experienced a critical drought situation. Also, in the years 2000, 2008 and 2018, more than 60% of the area of the region was in a very severe drought situation. In general, most of the studied area is in the range of severe and severe drought classes, which requires attention to the optimal management of water resources in these areas.

The aim of this study is to develop sensitive gully erosion models by implementing a machine learning algorithm (Support Vector Machine and Boosted Regression Tree) in the Moher basin. First, gully areas are identified, and then 13 variables predisposing to gully erosion (Slope, Slope Direction, Topographic Wetness Index, Streem Power Index, Terrain Ruggedness Index, Distance from Waterway, Drainage Density, Distance from Road, Land use, NDVI, Avera annual Rainfall, Geology, and Soil Texture) were selected. The variance inflation coefficient was used to evaluate multicollinearity between variables. Finally, a gully erosion sensitivity map was prepared in the environment (R). Also, the effect of physical and chemical characteristics of soil on gully erosion was investigated using Multivariate Regression. Regarding the importance of variables, Geology has the most significant effect on gully erosion in the SVM model, Land use, and the BRT model. The predicted sensitivity map was validated with the help of the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) in the Support Vector Machine and Boosted Regression Tree models were calculated as 0.92 and 0.94, respectively, which led to accurate prediction. Also, the results showed that the sand variable (9.299), sodium absorption ratio (7.967), and TNV (6.185) have the most significant effect on gully erosion

Soil erosion has been increasing in Iran in the last decade due to the lack of optimal use of pasture and forest lands. This factor destroys the ecological conditions suitable for the life of organisms while wasting the soil and producing sediment. Preservation and development of vegetation in pastures, such as medicinal plants, plays a significant role in preventing soil erosion. Salvia macrosiphon Boiss is a perennial species and has an essential oil plant of the Lamiaceae family, which is traditionally used in the treatment of respiratory diseases, depression and urinary tracts. Considering that in recent years, due to the economic and export value of this species, its natural habitats have been overexploited, and also due to climate changes and extensive droughts of the past few years, as well as harvesting in this way, the process of soil erosion in its natural habitats has been accelerated. Therefore, the present study was conducted with the aim of investigating 19 ecological attributes of the main habitats of S. macrosiphon in Fars and Hormozgan provinces.

In the current study, the natural habitats of this species were determined by using Flora Iranica and with the assistance of Hormozgan Agricultural Research, Education and Extension Organization experts. Characteristics of five habitats in Fars province (including Kazeroon, Farashband, Dehram, Evaz and Jahrom) and three habitats in Hormozgan province (including ZahedMahmoud, Bekhan and Sirmand) were studied. The climatic features of each natural habitat such as latitude and longitude, altitude, mean annual temperature, minimum and maximum temperature, and mean annual precipitation were recorded. From each habitat, three soil samples were taken from a depth of 0-30 cm. The percentages of clay, silt, sand, pH, electrical conductivity (EC), organic carbon, absorbable phosphorus, absorbable potassium, total nitrogen, calcium carbonate equivalent (CCE), absorbable iron, absorbable zinc, absorbable manganese and absorbable copper were measured. To analyze the studied environmental factors and the measured soil parameters, the multivariate analysis method including Pearson correlation coefficient of traits, decomposition into principal components and cluster analysis was used by SPSS ver. 26 software.

The results revealed that S. macrosiphon was distributed at an altitude of 400 to 1550 meters above sea level in Kazeroon, Farashband, Dehram, Evaz, Jahrom, ZahedMahmoud, Bekhan and Sirmand natural habitats and on the slope between 0 to 20%. The maximum and minimum annual rainfall were recorded as 156.2 and 387.7 mm, respectively. The average annual temperature was recorded at 24.5 °C and also, the minimum and maximum temperatures were -3.8 and +49.5, respectively. This species grows in the loam, sandy loam and silt loam soil textures with a pH of 7.7-8.4, an EC of 0.47-6.87 dS/m. Furthermore, it was found that S. macrosiphon was spread in non-saline and saline soils (electrical conductivity 0.47 to 6.87 dS/m). The soil in which the species was grown in the studied areas was poor in terms of available potassium content (46 to 302 ppm). The highest concentration of absorbable Fe (6.7 mg/kg) in Farashband habitat, absorbable Zn (3.8 mg/kg) in Kazeroon habitat, absorbable Mn (0.28 mg/kg) in Kazeroon habitat and absorbable Cu (0.2 mg/kg) was observed in Zahedmahmoud habitat. On the other hand, the lowest concentration of absorbable Fe (1.5 mg/kg), absorbable Zn (0.6 mg/kg) and absorbable Mn (3.3 mg/kg) was observed in Dehram habitat. Also, the lowest amount of absorbable Cu (0.3 mg/kg) was obtained in the two habitats of Dehram and Evaz. Using cluster analysis, habitats were divided into four clusters according to which habitats with common characteristics were placed in the same group.

The findings of this study showed that S. macrosiphon is resistant to hot and dry conditions. The two habitats of Dehram and Zahedmahmoud are prone to flooding due to the high percentage of silt in the soil texture. A high percentage of silt increases soil runoff during rainfall because the silt texture of the soil is one of the factors that prevents water from penetrating the soil. If the percentage of silt in the soil texture is high, the soil can absorb less water, and as a result, more water flows during rainfall, which can lead to soil erosion. The lack of absorbable phosphorus and potassium, as well as the lack of nitrogen and organic carbon, are the most obvious attributes of the soil of the S. macrosiphon habitats, which may cause limitations in its growth. The parent material of the soils of the studied areas originates from calcite limestone. In areas where the soil is calcareous, the temperature and pH are high. Based on the estimated Pearson correlation coefficient between the traits, it was determined that the higher organic carbon soil will have the higher amount of nitrogen. The presence of organic matter and the amount of nutrients have a direct relationship. This means that wherever the amount of organic matter is higher, the amount of nutrients is also higher. On the other hand, an increase in temperature leads to a decrease in soil organic matter. The results of analysis into the main components led to the extraction of five factors, which, according to the effectiveness of the variables, the selection based on the first component will lead to habitats with heavier soil texture and higher potassium concentration, which can provide a possibility of pasture development by this species in soils with a higher clay content and wide range of temperature fluctuations. In general, this research demonstrates considering the importance of S. macrosiphon in terms of being an under-expected pasture and medicinal species, as well as taking into account limitations such as low rainfall, and irregular spatial and temporal changes. Due to (limited) rainfall, high evaporation and transpiration and unprincipled exploitation, the condition of the habitats is extremely fragile and will lead to the instability of these ecosystems. By planning and conducting optimal management of pasture and medicinal resources through the obtained information, it is possible to domesticate and cultivate this plant by following the microclimatic conditions of this plant and providing natural conditions for its growth and protecting its valuable genetic resources. After all, these studies help us to be able to use optimal methods to improve and preserve the soil and prevent its erosion through plant preservation.

The main effect of vegetation in the rivers course is on the flow velocity. The velocity is affected by different vegetation type such as shrub, bush and grass-like in the bed, river side and floodplain. The water velocity at a river channel cross-section tends to decrease, due to the effects of vegetation stems and leaves by the flow resistance. As the flow velocity decreased, the water depths increase, which results the backwater and flooding of the floodplains. The regular and irregular (typical of many natural channels) geometric shape are different cross section types within the reach, which affect the flow characteristics especially during floods. The Manning’s n is a coefficient which characterizes the roughness of the river channel cross section and longitudinal variations. Manning’s n-values are often defined from tables, but can be estimated from field measurements. The selection of a Manning’s roughness coefficient value is depending on the expert judgment and can strongly affect computational results of the river flow velocity and discharge. In the present study, the effect of vegetation cover and the affecting factors of Manning roughness coefficient and flow velocity estimation were investigated. The aim of this work is to study the variation of Manning roughness coefficient (n) with the distance and flow river geometry along a river flowing in a low-lying plain.

In order to conduct this research, the characteristics of the vegetation and water flow velocity were recorded through field survey in 24 cross sections. The study area was a reach of the Qarehsou River, (from Anzab to Poleh Samian locations in a 16.70 km river length). Field measurements of flow velocity and evaluation of Qharahsoo riverbed were performed in May 2016. It should be noted that in the mentioned season, the river flow has a moderate flow that is fed through tributaries that originate from the forest area of Fandolo, Abi Begloo and upstream of Namin. The type and density of vegetation in the studied periods is different and is a combination of shrubs (turmeric), herbaceous plants (grasses, weeds) and in some cases natural trees (willow) or plantation (poplar). In the present study, the effect of vegetation on flow velocity has been evaluated through the instructions in the standard table for estimating vegetation conditions in calculating the roughness coefficient. Then the manning coefficient were estimated using the Cowan method for each river cross-section. Also, the relationship between vegetation related and flow depended variables inter-relationship were tested using Pearson correlation method in the SPSS software.

The values of Manning’s roughness coefficient, n found in this study is in the range of 0.0275 to 0.0831. According to the results, the flow depth had a significantly inverse relationship (r=-0.889, r=-0.934, p<0.01) with the velocity and the channel width. While, a negative significant correlation (r=-0.375, p<0.05) exists between the flow depth and manning roughness coefficient. Also, stream width had a negative correlated with Manning coefficients (r =-0.387, p<0.05), and had a direct relationship with flow velocity (r =0.941 and p<0.01). The value of manning roughness coefficient decrease, the amount of flow velocity, discharge, hydraulic radius, and flow width increases (r=-0.347, r=-0.474, r= -0.412, r=-0.387, with p<0.05 significance level). The variations of n manning roughness is due to changes of the river cross-sections, the flow depth and velocity, and the irregularities of the bed forms which affect the estimated flow velocity.

Also, the results showed that the estimated and measured velocity had an acceptable agreement, which is proved using the Pearson correlation coefficient. The accuracy of the velocity estimation indicates the ability of the Cowan method in manning roughness coefficient estimation and flow velocity. River channel geometry and stream flow characteristics are inter-related in natural channels with irregular condition. Variations in the geometry and vegetation of the river channel can impact stream velocity and resulted discharge estimation. In general, based on the results of the range of computational values, the minimum and maximum Manning roughness coefficients of 0.0275 and 0.0836 show that the values presented in the range are close to the roughness coefficient of natural rivers. It should be noted that the studied Qharahsoor River reaches passes through the agricultural lands of Ardabil plain, so the impact of agricultural land use and human interventions in the destruction of vegetation is evident and is one of the cases that has changed the dynamics of flow and erosion. In many cases, agricultural lands have been plowed along the river, and the removal of vegetation has caused sloping shores and intensified erosion. The relationship between discharge variables and the rate of erosion along the river bank and the bed is one of the items that can be studied in future research. According to the results, Manning's coefficient variations is influenced by the river morphometry and the vegetation characteristics in the cross section of the river. The manning equation in the studied meander river provide reliable results for flow velocity estimation, flood routing and inundation simulation studies.

Knowledge of rangeland vegetation characteristics as well as factors affecting it in environmental planning, land management and sustainable development is very important. However, regional and up-to-date maps of pasture vegetation cover are not always available. In this study, in order to plot the vegetation cover percentage of the rangelands and monitor its changes in drought and wet periods, NDVI products of MODIS sensor during the years from 2000 to 2017 with a spatial resolution of 250 m and a 16-day time resolution, and The SPI drought index were used. The study area is the part of the rangelands located in the Southern province of Yazd. In 2013, in order to provide ground truth data, a field work was done to take the sampling rate of vegetation from the rangeland level in the study area. According to the results, the NDVI index has a good ability to map vegetation cover, so the coefficient of determination (R2) between this index and the sample points was 0.71. Based on the results, the average vegetation cover of the studied area was 11.3% during the years 2000 to 2017. The highest and lowest amount of vegetation cover in the study area was in 2000 and 2002, with moderate mild conditions and very severe drought, respectively (14.6% and 9.2% respectively). The most important factors influencing the vegetation cover in the study area are rainfall and drought periods, so that the coefficient of determination (R2) between the SPI drought index and the average vegetation percentage was 0.85. In general, based on the results there is a high potential for assessing and monitoring rangeland vegetation changes using satellite data and remote sensing technique.

Today, the use of remote sensing methods for measuring land surface temperature has got more popular. Because remote sensing provides the opportunity to estimate the temperature in every region accurately. This study aimed to estimate land surface temperature using remote sensing in the south of Urmia Lake, compare them with observed data, and analyze the relationship between estimated land surface temperature and vegetation cover.

Changes in temperature of the surface of the earth were investigated from 2000 to 2017 as well as their relationship with changes in vegetation and land use in agricultural regions. Then, thermal bands of Landsat 7 and OLI were used for measuring land surface temperature and converting DN to radians and brightness temperature. Moreover, NDVI was used for calculating emissivity and determining land use based using an object-oriented method. Then, the relationship between vegetation and land surface temperature was investigated using regression analysis.

The results showed that the observed and estimated land surface temperature had increased from 2000 to 2017 due to the changes in land use and vegetation cover. According to the results of linear regression analysis, there is a significant relationship between estimated and observed land surface temperature (R2 = 0.72). Furthermore, there is a significant negative relationship between land surface temperature and vegetation cover.

The results showed that remote sensing methods provide accurate results in estimating the surface temperature. Understanding the surface temperature and its relationship with various land uses helps planners and experts to make managerial decisions to protect natural resources and agricultural lands.

The plant community in an area is the most sensitive indicator of climate. A visual comparison of climate and vegetation on a global scale immediately reveals a strong correlation between climatic and vegetation zones and this relationship, of course, are not co-incidental.  The main object of this study is to reveal the spatiotemporal association between climatic factors andvegetation Cover (NDVI) incorporate MODIS and TRMM product in Kohkiloyeh O Boirahmad province of Iran. So that the in this paer we use MOD13Q1  of MODIS product as NDVI layer for study area. MOD11A2 as landsurface temperature and 3B43 TRMM as meanmonthly accumulative rainfall for study area during 2002 to 2012 in 0.25° spatial resolution also were used as climatic factors. We use the correlation and cross-correlation analysis in 0.95 confident level(P_value =0.05) to detection the spatial and temporal association between the NDVI and 2 climatic Factor(LST and rainfall). The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months the pixels which have the high value of NDVI are spatiallyassociated with the pixels which have highest value of LST (6 to 14C°).As can be seen in table 1. Season the spatial correlation among NDVI and LST is so high which is statistical significant in 0.99 confident level  in winter. In transient months such as May, October and November,(temperate months in study region ) the spatial correlation among NDVI and LST is falling to 0.30 to 0.35 which is not statistical significant in 0.95 confident level. Finally in summer season or warm months including Jun to September, we found the minimum spatial association among the NDVI and LST.. In temporal aspect we found that the maximum correlation between NDVI and LST simultaneously appears and not whit lag time. The spatial correlation of NDVI and TRMM monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time in remain months we don’t find any significant correlation between NDVI and rainfall.

Among all Earth’s ecosystems, arid and semi-arid regions (about 30% of the Earth’s land) have experienced significant degradation over the past century due to the intensive land use practices and the increasing effects of droughts and climate changes (Maynard et al., 2016). Remote sensing is capable of detecting several groups of disturbances and changes, and has been widely used as a toolto identify long-term changes. Recent technological advancements in the methodology of mapping and monitoring land cover changesprovide new opportunities for the utilization of satellite imageries with high temporal frequency. Image fusion technique has been applied in different fields of environmental science, such asmapping crop growth, studying daily pollution of water resources, studying patterns of short-time ecological changes, determining regions with short-term erosion risk, etc. Image fusion algorithms include color combinations in three bands ofRGBimages, statistical and multi-scale methods. The present study seeks toevaluate the efficiency of image fusion algorithms and select the best algorithm for mapping vegetation in SouthKhorasan Province.

Following the pre-processing ofLandsat 8 and MODIS images, six image fusion algorithms, including NNDiffuse, HPF, Brovey, Gram-Schmidt, PC and CN, were studied and evaluated usingdifferent statistical criteria. Three statistical indices, including Root MeanSquare Error(RMSE), Mean Absolute Error(MAE) and Mean Error (MEB)were usedto evaluate the aforementioned algorithms.Then, the best image fusion algorithm was used to merge two different images received from Landsat8 (30m) and MODIS (250m). Finally, two vegetation indices, including NDVI and HVCI, were usedto map vegetation in SouthKhorasan Province. 

Results indicate that all six algorithms used in the present research can improvespatial resolution of the merged images. Compared to other 5 algorithms, NNDiffusecan merge thered and NIR bands of Landsat 8 and MODISwith a relatively higher accuracy. Therefore,NDVI extracted from this algorithm has the lowest RMSE and MAE compared to the original Landsat 8images. NDVI obtained from thefusion algorithms used in systematic-random transects of three land uses (including agricultural, urban and pastures) indicate that the index obtained from NNDiffuse algorithm has a better conformitywith the NDVI obtained from the original Landsat 8image. Then,redand NIR bands of Landsat8 and MODIS were combined forsimultaneous mapping of NDVI and HVCI in the case study area. Overall, a great part of SouthKhorasan Province has a vegetation cover of less than 10% and 40-50%, vegetation cover is only limited to small parts of the study area (agricultural land use and gardens). 

Generally, accessing simultaneous satellite images with high spatial resolutions, such as the Landsat series, is considered to be a challenge in vast area. The present study took advantage of different algorithms for image fusion and vegetation mapping in South Khorasan Province. Image fusion techniques, such as integration of Landsat and MODIS images, can be very useful for mapping purposes. Evaluation of 6image fusion techniques indicated thatNNDiffuse algorithm is the most suitable method for mapping vegetation in the study area.

Dust storms as one of the environmental hazards of the arid regions of the globe, including the southern, southwestern, eastern and central parts of Iran, has caused many environmental problems that confirm the need for studying and crisis managing its in scientific and executive congresses. Therefore, the present study attempts to evaluate the effects of climate elements on temperature, precipitation, humidity, evapotranspiration and vegetation index on the frequency of dust storms in Yazd province during the period of 5 years (2009-2014).

So, after determining the synoptic stations of the area, the dust data were extracted based on the code of the present weather phenomena and the values of the climatic elements. In the next step, their spatial zonation was determined through the interpolation method. Then, using the MODIS images, EVI index data were calculated according to the principle of time matching. Finally, a variety of simple and multiple regression models were fitted to estimate the occurrence frequency of dust, and the most appropriate relationships with higher preference values were reported.

The results showed that there was a significant relationship between the total dust with evapotranspiration and relative humidity with a R square of 0.973 and 0.614 and the standard deviation of 24.104 and 92.477 at sig. level of 99% and 95%. Also, there is the maximum significant relation between external dust with evapotranspiration and relative humidity with a R square 0.968 and 0.621, and the standard deviation was estimated to be 0.173 and 75.427 at sig. level of 99% and 95%, respectively. Internal dusts with evapotranspiration and maximum temperature with a R square of 0.770 and 0.377 and standard deviation of 15.1751 and 64.22 have a significant relationship with sig. level of 95%. The results of the total, external and internal dust storms with climatic elements and vegetation cover showed a significant correlation with the R square of 0.994, 0.988 and 0.956 and the standard error of estimation of 18.13713, 24.2555551 and 10.49989 at sig. level of 99% and 95%, respectively, which indicates the systematic function of climatic elements and vegetation cover in the occurrence of dust.

Introduction: Special and sensitive place of plants as the basis of ecosystems and role of them in moderating hazards such as floods, erosion and pollution of water resources make us to understand the environmental variables affecting the growth and development of vegetation cover. In light of this understanding and maintaining the interweaving relations between environmental variables and vegetation cover, we will be able to maintain and support the live coverage of vegetation. In this context, geomorphic variables as special appearance of other environmental elements and factors have closely related to vegetation cover in mountainous catchments. Awareness of the role of geomorphic variables in distribution of vegetation cover requires analysis the spatial relationships and scientifically accurate spatial modeling. In this regard, the emergence and development of remote sensing (RS) and Geographic Information System (GIS) and access to digital maps of geomorphic variables have provided the development and implementation of predictive models in investiagting the spatial variations of vegetation cover. This study aimed to assess and determine the spatial geomorphic-vegetation relationships using the pixel-based spatial approach in Arasbaran catchments (3 catchments: Naposhtehchhay, Ilginehchay and Mardanqumchay). Arasbaran mountainous catchments, NW Iran, include worthwhile forest and range ecosystems maintaining the great storage of biodiversity and particular uncommon species. Materials and Methods: Our approach is based on spatial multiple regression analysis between geomorphological parameters and abundance of vegetation cover. In this regard, 27 geomorphomety parameters as independent variables and NDVI as the dependent variable were computed from Landsat imagery (ETM sensor) and SRTM digital elevation model (DEM). First, preprocessing operations including atmospheric correction (noise reduction) and geometric correction was performed on the sattellite image. DEM is preprocessed by removal of sinks in GIS environment. After radiometric and geometric corrections, raster layers of geomorphic parameters computed and prepared using GIS and SAGA softwares and NDVI layer computed using IDRISI software. It is necessary to normalize the scale of data (-1 - +1) because of various scales of the variables using the following formula: Xnormalize= x- x(min)/x(max) - x(min) In the formula, x: raw value of the variable; min (x): minimum of the variable; max (x): maximum of the variable. We use the SAGA for performing the multiple regression (stepwise method) with 0/01 signisicance level. Results and Discussion: Preliminary results of the regression analysis showed that many of geomorphological parameters had significant relations with vegetation cover in spite of low correlation coefficients. Independent variables that positively correlated to the dependent variable were as follows: slope, transformed aspect, slope position, earth surface convexity, plan curvature, profile curvature, convergence index, flow path length, flow accumulation, Melton ruggedness number. Independent variables were negatively correlated to the dependent variable were as follows: valley depth, elevation, topography position index, slope length, flow width. The results of rgression steps indicated that 8 parameters including valley depth, topography position index, elevation, slope, slope position, transformed aspect, earth surface convexity and general curvature were the most important inependent variables explained most of variance of the dependent variable. Final results of regression analysis showed that the best linear regression model abtained in Mardanqumchay catchment with 0/32 R2 value. In contrast, the weakest regression model is abtained in Naposhtehcay with 0/11 R2 value. It appears that Ilghinehcay catchment have moderate phytogeomorphic conditions having rgression model with 0/21 R2 value. It is found that there is a correspondence between ruggedness of catchments and prediction power and efficiency of the regression models. Conclusion: This study attempts to analyze the relationships between geomorphology and vegetation cover using a geographic information system (GIS) and remote sensing (RS) approach in Arasbaran catchments, NW Iran. Identification of the most important independent geomorphic variables and comparison of the regression models in order to select the best regression model provided from the spatial regression analysis. Geomorphic parameters including valley depth, topography position index, elevation, slope, slope position, transformed aspect, earth surface convexity and general curvature valley are the most effective independent variables for explaining the spatial variations of vegetation cover abundance. The selected geomorphic variables, in the Whole, are enough reflection of geomorphology of a site, having not only the relation between form and process in them, being the special representative of other environmental factors. Comparison of the ruggedness of catchments with prediction power and efficiency of the regression models is interesting result of the research stressed the close and interweaved relationships between geomorphology and vegetation cover in the study area. Overall, although significant portion of the spatial variations of the vegetation cover abundance could not be explained by final regression models, but the predictive models can discover and determine important variables that affect the spatial patterns of vegetation cover and processes underlined in the patterns, leading to inhance understanding the geomorphic-vegetation relationships, considering the comprehensive spatial approach in regression analysis in one hand and complex non-linear relationships between vegetation cover and geomorphology in other hand.

  Over the past few decades, various vegetation indices derived from the reflection of various satellite wavelengths (generally a combination of near infrared and infrared bands) have been used to estimate biophysical characteristics of vegetation such as leaf area index (LAI), biomass, Plant growth and percentage of coverage, each of which, depending on the conditions in the study area, has shown good results (Qi et al.,1994; Rondeaux et al., 1996; Huete et al., 1997; Rouse et al., 1974) Generally, vegetation density is affected by a variety of environmental conditions such as climate, soil, geology and geomorphology (Abbate et al., 2006)

Determination of geomorphologic units, types and facies Providing vegetation map To prepare a vegetation map, Landsat 8 satellite imagery was prepared from Google Earth in 2017 and previewed images containing geometric and radiometric corrections. The NDVI index was extracted from 4 and 5 bands of Landsat 8 satellite images of Zilberchay watershed and classified into 12 classes. In the next step, the canopy measurements in the studied area were carried out within a representative area along the transect line. The purpose of vegetation is the shading level of any one. For this purpose, 1 × 1 m plot was used for vegetation diversity and vegetation form.


Calculation of soil gradient in each geomorphologic unit
In this study, in order to calculate the soil line equation, the geomorphic units were matched with satellite imagery. In each geomorphologic unit 60 pixels and in the amount of 720 pixels of soil were extracted using the position of geomorphic units and by plotting the reflection values of these pixels in the range The red and infrared bands near the soil line coefficients were calculated for each unit of geomorphology.

Calculation of correlation coefficient
To study the type and severity of relationships between geomorphic units and vegetation of the region, as well as the slope of the correlation coefficient line between them. Pearson correlation coefficient was calculated between vegetation values and gradient of soil line and geomorphic units (after encoding them).

Since there is a reverse relationship between vegetation and slope of the soil, geomorphology of Zilberchay watershed has a positive correlation with the vegetation cover and showed a negative correlation with the gradient of the soil coefficient. After calculating the correlation coefficients for each geomorphology unit, Qt had the highest negative correlation with the slope of the soil line to -0.988 and positive correlation with vegetation was 0.18 and the mic unit with soil gradient correlation There was no significant difference in the level of 0.01, while the vegetation showed positive correlation of 0.39. Also, the hio unit with the gradient of soil and vegetation cover was Pearson correlation coefficient of -0.45 and 0.62, respectively. The hio unit has more levels of rocky and vegetation-free extinction and a weaker correlation with other units.

Due to changes in soil characteristics, vegetation cover vegetation indices, which are presented in remote sensing sciences, often have errors. According to studies conducted by some researchers, the NDVI index can not quite accurately indicate the percentage of vegetation in arid regions, and the indicators that consider soil reflection can more accurately determine the percentage of vegetation in the study estimate (Darvishade et al., 2008). The findings of the research also show that each of the geomorphologic facies that have better vegetation cover have a more negative correlation with the soil line coefficient. Regular domain facies, in comparison with irregular domain facies that are better off of vegetation, have the same negative correlation with soil line factor. The soil factor coefficient is a Therefore, for the assessment of vegetation using remote sensing, it is better to use the modified indicators that have been applied to the land line, such as MSAVI, MCARI2, MTVI2 (Alavi Panah, 1390).for reducing the effects of spectral properties of soil on spectral reflections of the crown

Map information in research and studies is of great importance. The satellites are very suitable for the assessment of natural disasters because they have an extensive and integrated view, with a large part of the electromagnetic spectrum and up-to-date. The present study evaluates three classification (unsupervised, supervised and hybrid) methods for mapping vegetation in the Shalmanrud region. Using the ETM2002 satellite imagery, the 166-channel Land-Sat Satellite 34 in the ILWIS software version 3.1 was compiled,After performing the necessary corrections and initial processing, data classify was performed. Finally evaluated, efficiency of classification methods using index User accuracy overall accuracy and kappa coefficient. After checking the numbers obtained the index Comparison of three classification methods with the ground truth map showed that the supervised classification method using the Maximum likelihood method with a total accuracy of 67.84% and kappa coefficient of 0.6752 had better results than the other two methods. Also, analysis of the accuracy of each classification and comparison with ground truth map showed that the supervised classification method with a precision of 75.14% has the best result compared to the other two methods for the studied area.

  land surface temperature plays a vital role in a wide range of scientific researches, inter alia, climatology, meteorology, hydrology, ecology, geology, medical sciences, design and optimization of transportation services, fire location and especially in calculating the real evaporation and transpiration. There are some determining factors which affect the land surface temperature, such as, the kind of surface elements, topography conditions, environmental conditions, climate condition and the amount of emitted energy from the sun. Recognition and analysis of the relation between the land surface temperature and various factors are so critical. The remote sensing method has a widespread application in preparing the land surface temperature images due to the extensive covering and continuous data. The purpose of this study was to investigate the effects of vegetation cover indices and topographic factors on land surface temperature and modeling the relationship between land surface temperature, topographic conditions and vegetation cover using Landsat 8 satellite imagery. In this study, sensor reflective bands OLI, Jimenez and Sobrino method were utilized to calculate the emissivity of the available phenomena in the area. By using TIRS land set 8 sensor thermal bands 10 and 11 and utilizing Split-window algorithm, the land surface temperature was calculated. Topography parameters, such as elevation, slope and slope aspect and area vegetation were extracted using digital elevation model and NDVI index, respectively. Then, the relation between the land surface temperature and topography factors in diverse conditions was investigated by statistical analysis, and then, the validity of relations was analyzed with a confidence level of 95%. For this purpose, ENVI 5.3, Arc GIS 10.4, ERDAS IMAGING 2014 software as well as SPSS statistical software were used. The obtained resultants indicate that the study area has a uniform vegetation cover in most areas and a high percentage of areas have the NDVI of 0.45-0.6. Nonetheless, due to the diversity in topographic and climatic conditions the area surface temperature is inhomogeneous and non-uniform. Consequently, there is no relation with a high correlation coefficient between the land surface temperature and vegetation in the area. However, there is a reverse linear relation between the land surface temperature and vegetation in the area. This relation gains a higher correlation coefficient in the form of linear relation compared to second order polynomial, Pearson, logarithmic etc. equations. Areas with southern and southeast slope have higher land surface average temperature compared to other directions during imaging due to their position which is in the direction of sun straight radiation. The temperature average is different in various slopes. Making the relation of temperature and elevation independent of slope parameters and slope aspect, give rise to an increase in the correlation coefficient between two parameters. The relation of the land surface temperature and elevation, regardless of slope and slope aspect for the studied area, is a reverse linear relation with the correlation coefficient of 0.54, whereas for the relation between the land surface temperature and elevation in the western slope direction and slope of 40-50 degree, there is a reverse linear relation with the correlation coefficient of 0.76. Moreover, in investigating the relation between the land surface temperature with topographic conditions, simultaneous consideration of both elevation and slope variables as independent variables for modeling the dependent variable of surface temperature lead to a strengthened relation. The addition of vegetation index parameter to relation independent parameters bring about a rise in relation’s correlation coefficient. For instance, relation’s correlation coefficient of the land surface temperature with elevation independent variables, slope, and vegetation in the western, northwest and southeast direction, are 0.84, 0.81 and 0.8, respectively. All the obtained relations are investigated in the confidence level of 95%, and validity of relations was confirmed by “t” statistic and resultant probe for relations’ coefficients. Results imply that by considering each elevation, slope, slope aspect and NDVI parameters independently for modeling the land surface temperature, adverse results would be obtained, and by simultaneous using of both topographic parameters and vegetation and also their combination, as dependent parameters, the land surface temperature can be precisely calculated. In addition, for accurate modeling of the land surface temperature all topographic, climatic and environmental conditions for the area should be taken into account. The thermal and reflective remote sensing technology are economical, fast and effective due to several positive aspects such as providing uniform topographic, vegetation data and environmental parameters. So, further researches and investigation are necessary.

Introduction Special and sensitive role of vegetation cover in ecosystem sustainability and moderating hazards such as floods, erosion and pollution of water resources persuades us to understand the environmental variables affecting the growth and development of it. This issue particularly is important for susceptible mountainous catchments. In these environments, geomorphic variables as special representative of environmental factors have close and interweaved relation to vegetation cover. So, knowledge of the relationships between geomorphology and vegetation can help us to better manage and maintain the mountainous ecosystems. The understanding requires analysis the spatial relationships and scientifically accurate spatial modeling. In this regard, the emergence and development of remote sensing (RS) and geographic information system (GIS) have widely improved modelling the spatial variations of vegetation cover. However, a few issues that are fundamental and important in geomorphic-vegetation relations must be noticed. Maybe, the scale is the most important issue in phytogeomorphic researches. This study aimed to assess and determine the relationships between geomorphology and vegetation cover using spatial regression approach in Arasbaran catchments (3 catchments: Naposhtehchhay, Ilginehchay and Mardanqumchay). We have particular stress for effect the scale on the relations and comparison of predictive regression models in multiple scales based on catchment and subcatchment divisions.
Materials and Methods Our approach is based on spatial multiple regression analysis between geomorphologic parameters and abundance of vegetation. In this regard, 27 geomorphomety parameters as independent variables and Normalized Difference Vegetation Index (NDVI) as the dependent variable were computed from Landsat imagery (ETM sensor) and digital elevation model (DEM) SRTM. First, preprocessing operations including atmospheric correction (noise reduction) and geometric correction were performed on the sattellite image. DEM is preprocessed by removal of sinks in GIS environment. After radiometric and geometric corrections, raster layers of geomorphic parameters extracted and prepared using GIS and SAGA softwares and NDVI layer computed using IDRISI software. Furthermore, determination and mapping the cathments and subcatchments performed by ArcHydro extension of GIS.Given the various scales of the variables, it was necessary to normalize the scale of data ( ) using the following formula: In the formula, x: raw value of the variable; min (x): minimum of the variable; max (x): maximum of the variable.We use the SAGA for performing the spatial multiple regression (stepwise method) with 0/01 signisicance level. We examine the regression relations in two scales: 1- catchments 2- subcatchments. Finally, we compare different spatial multiple ression models at 2 scale for selection of best models.
Results and Discussion Initial results of showed that many of geomorphological parameters had significant relations with vegetation cover in spite of their low correlation coefficients. The results of rgression steps indicated that 8 parameters including valley depth, topographic position index, elevation, slope, slope position, transformed aspect, earth surface convexity and general curvature are the most important inependent variables in explaining the variance of dependent variable at catchment scale. The best linear regression model was abtained in Mardanqumchay catchment (R2= 0/32) in among regression models. In contrast, the weakest regression model is abtained in Naposhtehcay catchment (R2= 0/11) in among regression models. It appears that Ilghinehcay catchment have moderate phytogeomorphic conditions having moderate rgression model (R2= 0/21) in among regression models. It is found that there is a correspondence between ruggedness of catchments and prediction power and efficiency of the regression models. The results of regression analysis at subcatchment scale were significantly different. At this scale, best regression models observed with 0/42, 0/51 and 0/62 R2 values in Naposhtehchhay, Ilginehchay and Mardanqumchay, respectively. In contrast, weakest regression models observed with 0/08, 0/15 and 0/13 R2 values in Naposhtehchhay, Ilginehchay and Mardanqumchay, respectively. Hence, not only there are many differences among subcatchments, but there is considerable difference between catchments and subcatchments in the respect of intensity of relations between geomorphic parameters and vegetation cover.
Conclusion Results of the research showed the geomorphic parameters including valley depth, topography position index, elevation, slope, slope position, transformed aspect, earth surface convexity and general curvature valley are the most effective variables in explaining the spatial variations of vegetation cover in Arasbaran catchments. The selected geomorphic variables, Wholly, are partly complete reflection of geomorphology of a site, not only keeping the relation between form and process, are the special representative of other environmental factors. Although significant portion of spatial variations of the vegetation cover could not be explained by final regression models at cathment scale, but the predictive models are valuable, considering the application of pixel-based spatial approach in regression analysis, in one hand and complex non-linear relationships between vegetation cover and geomorphology, in other hand. At subcatchment scale, some of these relations are stronger than catchment scale and regression models are more efficient, leading to inhance the understanding of relationships between geomorphology and vegetation. Furthermore, we can give preference to subcathments based on strength of regression relations (R2 rates), which determines the phytogeomorphic sensivity of them, in order to manage and support the mountainous ecosystems. It is concluded that important information about relationships between geomorphology and vegetation can be acquireed at multiple scales, simultaneously.

Rangelands are natural ecosystems whit native plant species cover and are suitable for grazing. Water, soil and vegetation have fundamental role in maintaining natural ecosystems such as rangelands, so, performing the researches that lead to the preservation of natural environment and human’s environment is essential. Proper and allowable utilization of rangelands in the range management projects is the important scientific and technical measure in the range management of Iran. Range management projects as improvement - restoration and reclamation guideline have particular importance in the natural resources’ organization. On the other hand, today, erosion and soil loss and sediment production has become one of the main problems in the human environment. Restoration of vegetation and its effects on reduce of erosion have been studied by Li, 2006, Zhou, et al. 2008, Zhanga et al. 2004, Abdelkrima, et al. 2013. Goal of improvement - restoration operations in natural ecosystems is recovery plant compositon and cover for more protection of water and soil and decrease of erosion soil, finally increase forage yield. Therefore, soil management for optimal utilization and reduce its degradation is essential. Mapping the rate of erosion and sediment yield and areas prioritization according to it is effective step for organization, the protection and utilization of the soil. The purpose of this study was estimating amount of soil erosion and then simulate the effects of improvement and restoration on soil loss in the Lar area (Mazandaran).
Material and Revised universal soil loss equation (RUSLE) was used within GIS. The parameters this model are consists of R, K, LS, C and P, respectively, that are calculated with using the data of rainfall, soil maps, digital elevation models and remote sensing techniques, respectively. Suitable location of improvement - restoration projects was determinate based on maps of slope, elevation vegetation, pedology and rangeland condition. Then, by combining theses maps and applying basic principles of range management for suggest appropriate methods, was offered rangeland management model for improving or maintaining the optimal status. Then erosion risk map was prepared using revised universal soil loss equation. Finally change of amount of erosion after restoration and reclamation operations was predicted using this model in GIS.
The results showed that the mean values of parameters R, K, LS, C and P for study area was 67.143, 18.0, 52.5, 37.0 and 1, respectively. Average of yearly sediment load was estimated 51 ton/ha/year. Also the areas that to be allocated restoration and reclamation operations including 378 ha seeding, 246 ha inter seeding and 176 ha planting pile. Also cultivation on contour lines with seeding and strip cultivation with inter seeding was suggested and simulated. Simulation results after the suggested operations showed that the P and C factor will 0.8 and 0.31 respectively, therefore erosion mean will be reduced to 34 ton/ha/year and following that is equivalent 34% reduction. RUSLE model were used by many researchers in Iran and out of Iran (Asadi, et al. 1389, Vaezi, et al. 1389, Fiona, et al. 2010, Nijel, et al. 2010, Zangh, et al. 2010) and was confirmed its performance according this research. Results showed that areas with low slop had sediment rate than other areas. Average of yearly sediment load was zero to 595 ton/ha/year in study area. Soil erosion was more in eastern south parts of watershed rather than other parts, similar to results of Asadi, et al. 1389. Restoration and reclamation operations have changed C factor that can caused reduction of erosion. After simulation these operations had reduced erosion amount zero to 464 ton/ha/year, similar to results of Ligdi & Morgan, 1995, Terranva, et al. 2009, Stevens, et al. 2009.
Simulation results after the suggested operations showed that the P and C factor will 0.8 and 0.31 respectively, therefore erosion mean will be reduced to 34 ton/ha/year and following that is equivalent 34% reduction that indicates the importance of improvement - restoration projects within rangelands. Combination of GIS with sediment and erosion models is the effective method for determination of spatial distribution of sediment and erosion. Suggested improvement – restoration operations in addition to protection of water and soil will caused increase of forage product and following it increase of livestock product and increase of household income.

Soil erosion by water is a worldwide environmental problem which degrades soil productivity and water quality, causes sedimentation and increases the probability of flood. Soil erosion is detachment and transmission of soil particles by erosive factors like water and wind.Different studies have indicated the positive effect of vegetation cover in regulation of hydrological processes of above ground and decrease of runoff and soil loss(Zheng, 2006;Zhou et al., 2008; Vásquez-Méndez et al., 2010;Ouyang et al., 2010;Zhang et al., 2010;Nunes et al., 2011andWildhaber et al., 2011).To show the role of vegetation cover can be stated that decrease of vegetation cover due to overgrazing and deforestation cause detachment of soil aggregate stability which increase risk of runoff and soil loss.This study was carried out to assess the effects of different amounts of vegetation cover on controlling of runoff and soil loss in Nesho rangeland which placed on Mazandaran province, using rainfall simulator.

Forthis research,anarea about6 hectares, which includesdifferent vegetationcover percentages (minimum, moderateandmaximum) was selected. On the 110 sample points by a systematic sampling strategy on a regular grid spacing of 30 × 30 m, runoff and sediment yield samples were gathered using simulated rainfall experiment with 2 mm/min intensity and 11 minutes duration at 0.09 m2 plot and all the samples were transported to laboratory. Vegetation covers were measured in sample points using plot method.

Resultsof statistical analysis using one-way ANOVA and compare means (Duncan method) showed thatdifferent vegetationcover percentageshad significant effect onrunoffandsedimentcomponentsinthe study area.Time to runoff generationin Maximumvegetation coverwasmore than moderate and minimum vegetation cover significantly.As maximum vegetation cover with delaying of runoff generation enhances water infiltration into the soil and decrease soil loss. Mean of runoff yield in different vegetation covers had significant difference. Minimum and maximum vegetation cover percentages has highest and lowest runoff yield (liter/meter2) respectively. The reason maximum vegetation cover has lowest runoff yield is vegetation cover protects soil,by intercepting rainfall reducethe energy of rainfall drops, runoff yield and soil loss. Minimum and maximum vegetation cover percentages had highest and lowest runoff coefficientrespectively. Different vegetation cover percentages had significant difference from sediment yield. In minimum vegetation cover, sediment yield was increased 6.8 and 1.99 times to maximum and moderate vegetation cover and maximum vegetation cover had lowest sediment yield. Maximum vegetation cover by intercepting rainfall,decreasesurface flow and runoff yield reduces soil loss. Sediment concentration had negative relationship with vegetation cover percentages and as vegetation cover increases sediment yield rates is less. Minimum and maximum vegetation cover percentages had highest and lowest sediment yield rates respectively.

The results of simple linear Regression showed that vegetation cover had reducer effect on runoff yield, runoff coefficient, sediment yield and sediment concentration rates but had multiplier effect on time to runoff generation. Soil erosion and runoff can be predicted based on percentages of vegetation cover with coefficient of determination of 0.71 and 0.69 respectively.Results showed that different vegetation cover percentages had significant effect on runoff and sediment yield components and maximum vegetation cover had highest effect on reducing of runoff and sediment yield.Therefore, according to positive effects of vegetation cover,payspecial attention to rangeland vegetation cover to reduce runoff and soil loss is felt and it is proposed biological and soil conservation programs to reduce losses of soil erosionin areas with high erosion sensitivity in this region.

Gully erosion is one of the most significant erosion types. This type of erosion is one of the most important sources of sediment in different regions of the world. Gullies often have different dimensions and complex characteristics and these characteristics may affect the distribution of vegetation. Topographic characteristics of gullies provide complex ecosystem for vegetation establishment. In this study was examined the relationship between morphometric characteristics of gullies and vegetation in Lamerd saline lands. Also according to the Hydrogeomorphic classification, along almost homogeneous gullies was measured valley width, valley depth, channel width, channel depth, channel slope, valley width / valley depth, channel width / channel depth, valley width / channel width, valley depth / channel depth, (valley width / valley depth) / (channel width / channel depth) and vegetation characteristics. Relationship between vegetation distribution and morphometric characteristic were evaluated by statistical tests. The results show that there is most vegetation cover (%) in medium size streams and small size streams have sparse vegetation cover in study area. Ta.spp plant type is associated with large width and depth gullies, Sa.ri-At.le plant type is correlated with medium size width and At.le-Al.ca-Pr.fr plant type is associated with small size width and depth gullies. There is a significant correlation between morphometric characteristics and vegetation cover. The results show that the gullies characteristics determine the percentage of vegetation cover. Differences in the distribution of plant type and plant species studied by multivariate statistical techniques. In conclusion, results of this study show that morphometric characteristics of gullies in saline lands determines vegetation types and plant distribution.

One of the important parameters، that reduce negative influence of the other known parameters، is cover vegetation. In this study were estimated cover vegetation influence in soil erosion of rasin basin in Kermanshah privance that is our study area. The first step is preparation map of NDVI obtained & category of cover vegetation by the landsat TM data. Then Revised Universal Soil Loss Equation with six factors (Annual Rainfall Erocivitie، Soil Erodibility، Vegetation cover & Toographic) & hydrophysic model with five factors (Rainfall، Topography، vegetation، area، erosion) Were used for evaluating & estimating of soil erosion in rasin basin. Ofter product data layut of models factors to estimating of influence of cover vegetation، each models twoist performance، with calculating ratio coverage & without calculating this ratio. For estimating of intensity erosion by hydrophysic model، the sediment load potential calculated in surface unit. Result indicated that erosion in different land cover is variable. With the influence of other factors in the different land cover، the influence of these factors can be changed. The highest erosion have been estimated in the south، south-west & north، north-east of rasin basin. Without calculating of cover vegetation by RUSLE equation erosion emount increase from 485 ton per hectar per year to 650 ton per hectar per year، & in hydrophysic model sediment load potential emount increase from 834/259 ton per km2 per year to 1900/56 ton per km2 per year.

One of the important parameters، that reduce negative influence of the other known parameters، is cover vegetation. In this study were estimated cover vegetation influence in soil erosion of rasin basin in Kermanshah privance that is our study area. The first step is preparation map of NDVI obtained & category of cover vegetation by the landsat TM data. Then Revised Universal Soil Loss Equation with six factors (Annual Rainfall Erocivitie، Soil Erodibility، Vegetation cover & Toographic) & hydrophysic model with five factors (Rainfall، Topography، vegetation، area، erosion) Were used for evaluating & estimating of soil erosion in rasin basin. Ofter product data layut of models factors to estimating of influence of cover vegetation، each models twoist performance، with calculating ratio coverage & without calculating this ratio. For estimating of intensity erosion by hydrophysic model، the sediment load potential calculated in surface unit. Result indicated that erosion in different land cover is variable. With the influence of other factors in the different land cover، the influence of these factors can be changed. The highest erosion have been estimated in the south، south-west & north، north-east of rasin basin. Without calculating of cover vegetation by RUSLE equation erosion emount increase from 485 ton per hectar per year to 650 ton per hectar per year، & in hydrophysic model sediment load potential emount increase from 834/259 ton per km2 per year to 1900/56 ton per km2 per year.